------------------------------------------------------------------------------------------------------------------------------
      name:  <unnamed>
       log:  /Users/rachaelhinkle/Library/CloudStorage/Dropbox/Citations_Matthews_Hinkle/code/MH_R&P_replication_files/MH_Judi
> cial_Influence.log
  log type:  text
 opened on:   2 Jun 2025, 10:01:46

. 
. *** Load dataset
. use "MH_Judicial_Influence_data.dta", clear

. 
. *** Overall probability of ciation is 0.0007
. tab nonneg_cite

nonneg_cite |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |  5,211,824       99.93       99.93
          1 |      3,735        0.07      100.00
------------+-----------------------------------
      Total |  5,215,559      100.00

. 
. *** Main model 
. probit nonneg_cite numSame sameAgeCohort cosine100 sameSubIssue id_opposed_prec  citing_chief citing_fresh_au ln_citing_word
> _count  citing_year cited_dissent cited_prec_age ln_cited_word_count,  robust cluster(citing_caseID)

Iteration 0:   log pseudolikelihood = -30781.239  
Iteration 1:   log pseudolikelihood = -29542.903  
Iteration 2:   log pseudolikelihood =  -25034.91  
Iteration 3:   log pseudolikelihood = -25016.784  
Iteration 4:   log pseudolikelihood = -25016.737  
Iteration 5:   log pseudolikelihood = -25016.737  

Probit regression                                    Number of obs = 5,215,559
                                                     Wald chi2(12) =   5388.30
                                                     Prob > chi2   =    0.0000
Log pseudolikelihood = -25016.737                    Pseudo R2     =    0.1873

                              (Std. err. adjusted for 3,695 clusters in citing_caseID)
--------------------------------------------------------------------------------------
                     |               Robust
         nonneg_cite | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
---------------------+----------------------------------------------------------------
             numSame |    .026028   .0081948     3.18   0.001     .0099664    .0420896
       sameAgeCohort |   .0306337    .011999     2.55   0.011     .0071161    .0541514
           cosine100 |   .0648415   .0009347    69.37   0.000     .0630096    .0666734
        sameSubIssue |   .3526605   .0213263    16.54   0.000     .3108616    .3944593
     id_opposed_prec |  -.0098103   .0117453    -0.84   0.404    -.0328305      .01321
        citing_chief |  -.0549168    .050755    -1.08   0.279    -.1543948    .0445612
     citing_fresh_au |  -.0900542   .0720361    -1.25   0.211    -.2312424     .051134
ln_citing_word_count |   .1683147   .0148207    11.36   0.000     .1392667    .1973627
         citing_year |  -.0109982   .0031487    -3.49   0.000    -.0171695   -.0048268
       cited_dissent |   .0239669    .014481     1.66   0.098    -.0044153     .052349
      cited_prec_age |  -.0177298   .0018796    -9.43   0.000    -.0214137   -.0140458
 ln_cited_word_count |   .0592766    .009606     6.17   0.000     .0404492    .0781039
               _cons |   16.17184   6.317553     2.56   0.010     3.789659    28.55401
--------------------------------------------------------------------------------------

. estimates store mod1

. 
. *** Figure 1
. margins, at(numSame=(0 1 2 3) ) post

Predictive margins                                   Number of obs = 5,215,559
Model VCE: Robust

Expression: Pr(nonneg_cite), predict()
1._at: numSame = 0
2._at: numSame = 1
3._at: numSame = 2
4._at: numSame = 3

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
         _at |
          1  |   .0006196   .0000354    17.49   0.000     .0005502    .0006891
          2  |   .0006678    .000026    25.67   0.000     .0006168    .0007188
          3  |   .0007197   .0000213    33.82   0.000     .0006779    .0007614
          4  |   .0007755   .0000284    27.34   0.000     .0007199    .0008311
------------------------------------------------------------------------------

. marginsplot, xdimension(numSame) recast(scatter) plot1opts(bcolor(gs10) lcolor(black) ) ci1opt(color(black) lwidth(medium)) 
> yscale(range(0.0005 .0009)) ytick(0.0005(.00005).0009) ylab(0.0005(.0001).0009)  ytitle(Predicted Probability of Citation) x
> title("Number of Shared Identities") title("Probability of Citation", color(black)) legend(off) xsize(3) ysize(2.25) plotreg
> ion(margin(l+5 r+5)) graphregion(lcolor(black) lwidth(medthick) margin(r+5 b+3))

Variables that uniquely identify margins: numSame

. 
. ** Moving from 0 to 3 shared identities results in a relative increase of 25%
. estimates restore mod1
(results mod1 are active now)

. margins, at(numSame=(0 3)) 

Predictive margins                                   Number of obs = 5,215,559
Model VCE: Robust

Expression: Pr(nonneg_cite), predict()
1._at: numSame = 0
2._at: numSame = 3

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
         _at |
          1  |   .0006196   .0000354    17.49   0.000     .0005502    .0006891
          2  |   .0007755   .0000284    27.34   0.000     .0007199    .0008311
------------------------------------------------------------------------------

. di .0007755/.0006196
1.2516139

. 
. 
. *** Online Appendix A
. 
. * 9% of citations are negative
. tab lexCite

    lexCite |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |  5,211,450       99.92       99.92
          1 |      4,109        0.08      100.00
------------+-----------------------------------
      Total |  5,215,559      100.00

. count if lexNeg == 1 & lexPos == 0
  374

. di 374/4109
.09101971

. 
. ** Table 1 Summary of precedent-authoring judges
. tab citedRace if citedFemale == 0 & citedGOP == 1

   Race or Ethnicity |      Freq.     Percent        Cum.
---------------------+-----------------------------------
    African American |     16,559        0.58        0.58
      Asian American |      2,516        0.09        0.67
            Hispanic |     88,132        3.08        3.75
               White |  2,750,186       96.25      100.00
---------------------+-----------------------------------
               Total |  2,857,393      100.00

. tab citedRace if citedFemale == 1 & citedGOP == 1

   Race or Ethnicity |      Freq.     Percent        Cum.
---------------------+-----------------------------------
    African American |     25,541        6.90        6.90
            Hispanic |      3,445        0.93        7.83
               White |    341,370       92.17      100.00
---------------------+-----------------------------------
               Total |    370,356      100.00

. tab citedRace if citedFemale == 0 & citedGOP == 0

   Race or Ethnicity |      Freq.     Percent        Cum.
---------------------+-----------------------------------
    African American |    248,342       17.93       17.93
      Asian American |     20,803        1.50       19.44
            Hispanic |    120,002        8.67       28.10
               White |    995,562       71.90      100.00
---------------------+-----------------------------------
               Total |  1,384,709      100.00

. tab citedRace if citedFemale == 1 & citedGOP == 0

   Race or Ethnicity |      Freq.     Percent        Cum.
---------------------+-----------------------------------
    African American |     61,231       10.15       10.15
            Hispanic |      8,491        1.41       11.56
               White |    533,379       88.44      100.00
---------------------+-----------------------------------
               Total |    603,101      100.00

. 
. * Panel median matches case outcome 49% of the times
. count if (cited_pan_med < 0 & cited_liberal_outcome ==1) | (cited_pan_med > 0 & cited_conservative_outcome ==1)
  2,573,562

. di 2573562/_N
.49343934

. 
. ** Table 2: Summary Statistics
. eststo sumstats: estpost summarize numSame  cosine100 ln_citing_word_count   citing_year  cited_prec_age ln_cited_word_count
>  , d

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)  e(skewn~)  e(kurto~)     e(sum)     e(min)     e(max) 
-------------+--------------------------------------------------------------------------------------------------------------
     numSame |   5215559    5215559   1.994442   .7252823   .8516351  -.3243145   2.156586   1.04e+07          0          3 
   cosine100 |   5215559    5215559   6.075696   9.415376   3.068448   3.150495   28.70693   3.17e+07   1.285056   83.16205 
ln_citing_~t |   5215559    5215559   8.272665   .3726602    .610459   .0531262   3.290017   4.31e+07   5.805135   10.63783 
 citing_year |   5215559    5215559   2006.179   8.439676   2.905112  -.4508869   2.153866   1.05e+10       2000       2010 
cited_prec~e |   5215559    5215559   5.594932   13.98124    3.73915   .4690842    2.34886   2.92e+07          0         15 
ln_cited_w~t |   5215559    5215559   8.298743    .403067   .6348756   .0275735   3.233234   4.33e+07   5.805135   10.63783 

             |     e(p1)      e(p5)     e(p10)     e(p25)     e(p50)     e(p75)     e(p90)     e(p95)     e(p99) 
-------------+---------------------------------------------------------------------------------------------------
     numSame |         0          1          1          1          2          3          3          3          3 
   cosine100 |  2.154969   2.806351   3.243542    4.12724   5.436009   7.202237    9.48215   11.37331    16.9542 
ln_citing_~t |  6.826545   7.282761   7.496098   7.869784   8.276903   8.661813   9.053102   9.280519   9.791885 
 citing_year |      2000       2001       2002       2004       2007       2009       2010       2010       2010 
cited_prec~e |         0          0          1          2          5          8         11         12         14 
ln_cited_w~t |  6.841616   7.262629   7.488294   7.873979   8.296547   8.713582   9.097732   9.326522   9.876219 

. estout sumstats, cells("min(fmt(2)) p25(fmt(2)) p50(fmt(2)) p75(fmt(2)) max(fmt(2))") 

-----------------------------------------------------------------------------
                 sumstats                                                    
                      min          p25          p50          p75          max
-----------------------------------------------------------------------------
numSame              0.00         1.00         2.00         3.00         3.00
cosine100            1.29         4.13         5.44         7.20        83.16
ln_citing_~t         5.81         7.87         8.28         8.66        10.64
citing_year       2000.00      2004.00      2007.00      2009.00      2010.00
cited_prec~e         0.00         2.00         5.00         8.00        15.00
ln_cited_w~t         5.81         7.87         8.30         8.71        10.64
-----------------------------------------------------------------------------

. tab nonneg_cite

nonneg_cite |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |  5,211,824       99.93       99.93
          1 |      3,735        0.07      100.00
------------+-----------------------------------
      Total |  5,215,559      100.00

. tab sameGenderB

Same Gender |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |  1,653,379       31.70       31.70
          1 |  3,562,180       68.30      100.00
------------+-----------------------------------
      Total |  5,215,559      100.00

. tab sameRaceB

  Same Race |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |  1,146,569       21.98       21.98
          1 |  4,068,990       78.02      100.00
------------+-----------------------------------
      Total |  5,215,559      100.00

. tab sameParty

 Same Party |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |  2,444,598       46.87       46.87
          1 |  2,770,961       53.13      100.00
------------+-----------------------------------
      Total |  5,215,559      100.00

. tab sameAgeCohort

       Same |
 Generation |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |  3,107,334       59.58       59.58
          1 |  2,108,225       40.42      100.00
------------+-----------------------------------
      Total |  5,215,559      100.00

. tab sameSubIssue

   Same Sub |
      Issue |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |  1,909,739       36.62       36.62
          1 |  3,305,820       63.38      100.00
------------+-----------------------------------
      Total |  5,215,559      100.00

. tab id_opposed_prec 

Ideological |
 ly Opposed |
  Precedent |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |  3,647,137       69.93       69.93
          1 |  1,568,422       30.07      100.00
------------+-----------------------------------
      Total |  5,215,559      100.00

. tab citing_chief

Chief Judge |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |  5,088,576       97.57       97.57
          1 |    126,983        2.43      100.00
------------+-----------------------------------
      Total |  5,215,559      100.00

. tab citing_fresh_au

  New Judge |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |  5,102,078       97.82       97.82
          1 |    113,481        2.18      100.00
------------+-----------------------------------
      Total |  5,215,559      100.00

. tab cited_dissent

  Precedent |
        Not |
  Unanimous |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |  4,390,060       84.17       84.17
          1 |    825,499       15.83      100.00
------------+-----------------------------------
      Total |  5,215,559      100.00

. 
. ** Table 3: Citation Model Regression Results
. estout mod1, cells("b(star fmt(3)) se(fmt(3) par)")  starlevels(* 0.05)  stats( N aic bic)  label 

-----------------------------------------------
                             mod1              
                                b            se
-----------------------------------------------
nonneg_cite                                    
Number of Shared I~s        0.026*      (0.008)
Same Generation             0.031*      (0.012)
Cosine Similarity           0.065*      (0.001)
Same Sub Issue              0.353*      (0.021)
Ideologically Oppo~t       -0.010       (0.012)
Chief Judge                -0.055       (0.051)
New Judge                  -0.090       (0.072)
Logged Word Count,~n        0.168*      (0.015)
Opinion Year               -0.011*      (0.003)
Precedent Not Unan~s        0.024       (0.014)
Age                        -0.018*      (0.002)
Logged Word Count,~t        0.059*      (0.010)
_cons                      16.172*      (6.318)
-----------------------------------------------
N                     5215559.000              
aic                     50059.474              
bic                     50234.547              
-----------------------------------------------

. 
. 
. *** Online Appendix B
. 
. ** Table 4: Binary Specification of Shared Identities
. probit nonneg_cite  i.ingroupBuck  sameAgeCohort cosine100 sameSubIssue id_opposed_prec citingPanAu citing_chief citing_fres
> h_au ln_citing_word_count  citing_year cited_dissent cited_prec_age ln_cited_word_count,  robust cluster(citing_caseID) 

Iteration 0:   log pseudolikelihood = -30781.239  
Iteration 1:   log pseudolikelihood = -29531.888  
Iteration 2:   log pseudolikelihood = -25027.193  
Iteration 3:   log pseudolikelihood = -25008.892  
Iteration 4:   log pseudolikelihood = -25008.844  
Iteration 5:   log pseudolikelihood = -25008.844  

Probit regression                                    Number of obs = 5,215,559
                                                     Wald chi2(19) =   5460.03
                                                     Prob > chi2   =    0.0000
Log pseudolikelihood = -25008.844                    Pseudo R2     =    0.1875

                                       (Std. err. adjusted for 3,695 clusters in citing_caseID)
-----------------------------------------------------------------------------------------------
                              |               Robust
                  nonneg_cite | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
------------------------------+----------------------------------------------------------------
                  ingroupBuck |
                 Same Gender  |  -.0169965   .0325155    -0.52   0.601    -.0807257    .0467326
                   Same Race  |   .0419928   .0275034     1.53   0.127    -.0119129    .0958984
                  Same Party  |  -.0070142   .0384031    -0.18   0.855     -.082283    .0682545
        Same Gender and Race  |    .033936   .0286921     1.18   0.237    -.0222995    .0901714
       Same Gender and Party  |    .027688   .0375939     0.74   0.461    -.0459947    .1013707
         Same Race and Party  |   .0686775   .0275118     2.50   0.013     .0147554    .1225997
Same Race, Gender, and Party  |   .0708747   .0274349     2.58   0.010     .0171031    .1246462
                              |
                sameAgeCohort |   .0302539   .0120339     2.51   0.012     .0066679    .0538399
                    cosine100 |    .064836   .0009335    69.45   0.000     .0630063    .0666657
                 sameSubIssue |    .353315   .0213656    16.54   0.000     .3114393    .3951907
              id_opposed_prec |  -.0075758   .0118083    -0.64   0.521    -.0307196    .0155681
                  citingPanAu |   .4138066   .1962712     2.11   0.035     .0291221    .7984912
                 citing_chief |  -.0554498   .0507457    -1.09   0.275    -.1549096    .0440099
              citing_fresh_au |  -.0917847   .0720329    -1.27   0.203    -.2329665    .0493972
         ln_citing_word_count |   .1691135   .0148248    11.41   0.000     .1400575    .1981695
                  citing_year |  -.0109544   .0031379    -3.49   0.000    -.0171047   -.0048041
                cited_dissent |   .0237656   .0144957     1.64   0.101    -.0046455    .0521767
               cited_prec_age |  -.0177379   .0018804    -9.43   0.000    -.0214234   -.0140525
          ln_cited_word_count |   .0603841   .0096136     6.28   0.000     .0415419    .0792264
                        _cons |   16.07719   6.294487     2.55   0.011     3.740222    28.41416
-----------------------------------------------------------------------------------------------

. estimates store mod2

. probit nonneg_cite  c.sameGenderB c.sameRaceB c.sameParty sameAgeCohort cosine100 sameSubIssue id_opposed_prec citingPanAu c
> iting_chief citing_fresh_au ln_citing_word_count  citing_year cited_dissent cited_prec_age ln_cited_word_count,  robust clus
> ter(citing_caseID)  

Iteration 0:   log pseudolikelihood = -30781.239  
Iteration 1:   log pseudolikelihood = -29532.109  
Iteration 2:   log pseudolikelihood = -25028.211  
Iteration 3:   log pseudolikelihood = -25010.001  
Iteration 4:   log pseudolikelihood = -25009.954  
Iteration 5:   log pseudolikelihood = -25009.954  

Probit regression                                    Number of obs = 5,215,559
                                                     Wald chi2(15) =   5423.10
                                                     Prob > chi2   =    0.0000
Log pseudolikelihood = -25009.954                    Pseudo R2     =    0.1875

                              (Std. err. adjusted for 3,695 clusters in citing_caseID)
--------------------------------------------------------------------------------------
                     |               Robust
         nonneg_cite | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
---------------------+----------------------------------------------------------------
         sameGenderB |  -.0041681   .0156232    -0.27   0.790    -.0347889    .0264527
           sameRaceB |   .0486777   .0200709     2.43   0.015     .0093395    .0880159
           sameParty |   .0364238   .0112959     3.22   0.001     .0142842    .0585634
       sameAgeCohort |     .03056   .0119976     2.55   0.011     .0070451    .0540749
           cosine100 |   .0648312   .0009337    69.43   0.000     .0630011    .0666612
        sameSubIssue |   .3531383    .021365    16.53   0.000     .3112637     .395013
     id_opposed_prec |  -.0085342   .0117311    -0.73   0.467    -.0315267    .0144583
         citingPanAu |   .4157443   .1962703     2.12   0.034     .0310617     .800427
        citing_chief |  -.0528067   .0507217    -1.04   0.298    -.1522193     .046606
     citing_fresh_au |  -.0901252   .0720803    -1.25   0.211    -.2314001    .0511497
ln_citing_word_count |   .1682673   .0148313    11.35   0.000     .1391985     .197336
         citing_year |  -.0109552   .0031405    -3.49   0.000    -.0171105   -.0047999
       cited_dissent |   .0232761   .0144932     1.61   0.108      -.00513    .0516822
      cited_prec_age |   -.017675   .0018808    -9.40   0.000    -.0213613   -.0139888
 ln_cited_word_count |   .0596002    .009607     6.20   0.000     .0407708    .0784295
               _cons |   16.07927   6.300846     2.55   0.011     3.729839     28.4287
--------------------------------------------------------------------------------------

. estimates store mod3

. estout mod2 mod3, cells("b(star fmt(3)) se(fmt(3) par)")  starlevels(* 0.05)  stats( N aic bic) label 

--------------------------------------------------------------------------
                             mod2                       mod3              
                                b            se            b            se
--------------------------------------------------------------------------
nonneg_cite                                                               
No Shared Identities        0.000           (.)                           
Same Gender                -0.017       (0.033)                           
Same Race                   0.042       (0.028)                           
Same Party                 -0.007       (0.038)                           
Same Gender and Race        0.034       (0.029)                           
Same Gender and Pa~y        0.028       (0.038)                           
Same Race and Party         0.069*      (0.028)                           
Same Race, Gender,~y        0.071*      (0.027)                           
Same Generation             0.030*      (0.012)        0.031*      (0.012)
Cosine Similarity           0.065*      (0.001)        0.065*      (0.001)
Same Sub Issue              0.353*      (0.021)        0.353*      (0.021)
Ideologically Oppo~t       -0.008       (0.012)       -0.009       (0.012)
Precedent Author o~l        0.414*      (0.196)        0.416*      (0.196)
Chief Judge                -0.055       (0.051)       -0.053       (0.051)
New Judge                  -0.092       (0.072)       -0.090       (0.072)
Logged Word Count,~n        0.169*      (0.015)        0.168*      (0.015)
Opinion Year               -0.011*      (0.003)       -0.011*      (0.003)
Precedent Not Unan~s        0.024       (0.014)        0.023       (0.014)
Age                        -0.018*      (0.002)       -0.018*      (0.002)
Logged Word Count,~t        0.060*      (0.010)        0.060*      (0.010)
Same Gender                                           -0.004       (0.016)
Same Race                                              0.049*      (0.020)
Same Party                                             0.036*      (0.011)
_cons                      16.077*      (6.294)       16.079*      (6.301)
--------------------------------------------------------------------------
N                     5215559.000                5215559.000              
aic                     50057.688                  50051.907              
bic                     50327.032                  50267.382              
--------------------------------------------------------------------------

. 
. ** Pairwise comparison from model with interacted binary shared identity variables
. estimates restore mod2
(results mod2 are active now)

. pwcompare ingroupBuck

Pairwise comparisons of marginal linear predictions

Margins: asbalanced

--------------------------------------------------------------------------------------------------------
                                                       |                                 Unadjusted
                                                       |   Contrast   Std. err.     [95% conf. interval]
-------------------------------------------------------+------------------------------------------------
nonneg_cite                                            |
                                           ingroupBuck |
                  Same Gender vs No Shared Identities  |  -.0169965   .0325155     -.0807257    .0467326
                    Same Race vs No Shared Identities  |   .0419928   .0275034     -.0119129    .0958984
                   Same Party vs No Shared Identities  |  -.0070142   .0384031      -.082283    .0682545
         Same Gender and Race vs No Shared Identities  |    .033936   .0286921     -.0222995    .0901714
        Same Gender and Party vs No Shared Identities  |    .027688   .0375939     -.0459947    .1013707
          Same Race and Party vs No Shared Identities  |   .0686775   .0275118      .0147554    .1225997
 Same Race, Gender, and Party vs No Shared Identities  |   .0708747   .0274349      .0171031    .1246462
                             Same Race vs Same Gender  |   .0589893   .0314693     -.0026894     .120668
                            Same Party vs Same Gender  |   .0099823   .0393343     -.0671115    .0870761
                  Same Gender and Race vs Same Gender  |   .0509325   .0280197     -.0039852    .1058501
                 Same Gender and Party vs Same Gender  |   .0446845   .0336859     -.0213387    .1107077
                   Same Race and Party vs Same Gender  |    .085674    .030902      .0251072    .1462408
          Same Race, Gender, and Party vs Same Gender  |   .0878712   .0264344      .0360607    .1396817
                              Same Party vs Same Race  |   -.049007   .0376793     -.1228572    .0248431
                    Same Gender and Race vs Same Race  |  -.0080568   .0248802      -.056821    .0407074
                   Same Gender and Party vs Same Race  |  -.0143048   .0379931     -.0887699    .0601604
                     Same Race and Party vs Same Race  |   .0266848   .0239987     -.0203518    .0737213
            Same Race, Gender, and Party vs Same Race  |   .0288819   .0229716     -.0161416    .0739053
                   Same Gender and Race vs Same Party  |   .0409502   .0384187     -.0343491    .1162495
                  Same Gender and Party vs Same Party  |   .0347022   .0439825     -.0515019    .1209064
                    Same Race and Party vs Same Party  |   .0756918   .0389318     -.0006132    .1519967
           Same Race, Gender, and Party vs Same Party  |   .0778889   .0377903      .0038213    .1519565
        Same Gender and Party vs Same Gender and Race  |   -.006248   .0340608     -.0730058    .0605099
          Same Race and Party vs Same Gender and Race  |   .0347416   .0237508     -.0118092    .0812923
 Same Race, Gender, and Party vs Same Gender and Race  |   .0369387   .0176781      .0022903    .0715871
         Same Race and Party vs Same Gender and Party  |   .0409895   .0371873     -.0318963    .1138754
Same Race, Gender, and Party vs Same Gender and Party  |   .0431867   .0344637     -.0243609    .1107343
  Same Race, Gender, and Party vs Same Race and Party  |   .0021971    .022796     -.0424823    .0468766
--------------------------------------------------------------------------------------------------------

. * Difference between 3 shared identities and shared gender and race, p = 0.037
. di 2*(1 - normal(.0369387/.0176781))
.03666116

. * Difference between 3 shared identities and shared party, p = 0.039
. di 2*(1 - normal(.0778889/.0377903))
.03929524

. * Difference between 3 shared identities and shared gender, p < 0.0001
. di 2*(1 - normal(.0878712/.0264344))
.00088697

. * Difference between shared party and race and only same gender, p = 0.006
. di 2*(1 - normal( .085674/.030902 ))
.00556374

. 
. 
. *** Online Appendix C
. 
. ** Table 5: Models without cosine similarity
. probit nonneg_cite numSame sameAgeCohort sameSubIssue id_opposed_prec  citing_chief citing_fresh_au ln_citing_word_count  ci
> ting_year cited_dissent cited_prec_age ln_cited_word_count,  robust cluster(citing_caseID)

Iteration 0:   log pseudolikelihood = -30781.239  
Iteration 1:   log pseudolikelihood = -29756.895  
Iteration 2:   log pseudolikelihood = -29653.102  
Iteration 3:   log pseudolikelihood = -29652.572  
Iteration 4:   log pseudolikelihood = -29652.572  

Probit regression                                    Number of obs = 5,215,559
                                                     Wald chi2(11) =    952.36
                                                     Prob > chi2   =    0.0000
Log pseudolikelihood = -29652.572                    Pseudo R2     =    0.0367

                              (Std. err. adjusted for 3,695 clusters in citing_caseID)
--------------------------------------------------------------------------------------
                     |               Robust
         nonneg_cite | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
---------------------+----------------------------------------------------------------
             numSame |     .03025   .0073762     4.10   0.000     .0157928    .0447071
       sameAgeCohort |    .021452   .0108608     1.98   0.048     .0001652    .0427388
        sameSubIssue |   .4375313   .0199845    21.89   0.000     .3983624    .4767003
     id_opposed_prec |  -.0045302   .0106422    -0.43   0.670    -.0253886    .0163283
        citing_chief |   .0055186   .0524898     0.11   0.916    -.0973595    .1083967
     citing_fresh_au |  -.1093517   .0682845    -1.60   0.109    -.2431869    .0244836
ln_citing_word_count |   .2059295   .0135532    15.19   0.000     .1793657    .2324933
         citing_year |  -.0128535   .0029127    -4.41   0.000    -.0185622   -.0071448
       cited_dissent |   .0513535   .0129302     3.97   0.000     .0260109    .0766962
      cited_prec_age |  -.0205931   .0017315   -11.89   0.000    -.0239868   -.0171994
 ln_cited_word_count |   .0648144   .0086064     7.53   0.000     .0479462    .0816826
               _cons |   20.02774   5.838604     3.43   0.001     8.584287    31.47119
--------------------------------------------------------------------------------------

. estimates store mod1c

. probit nonneg_cite  i.ingroupBuck  sameAgeCohort  sameSubIssue id_opposed_prec citing_chief citing_fresh_au ln_citing_word_c
> ount  citing_year cited_dissent cited_prec_age ln_cited_word_count,  robust cluster(citing_caseID)  

Iteration 0:   log pseudolikelihood = -30781.239  
Iteration 1:   log pseudolikelihood = -29747.495  
Iteration 2:   log pseudolikelihood = -29641.972  
Iteration 3:   log pseudolikelihood = -29641.426  
Iteration 4:   log pseudolikelihood = -29641.426  

Probit regression                                    Number of obs = 5,215,559
                                                     Wald chi2(17) =    963.06
                                                     Prob > chi2   =    0.0000
Log pseudolikelihood = -29641.426                    Pseudo R2     =    0.0370

                                       (Std. err. adjusted for 3,695 clusters in citing_caseID)
-----------------------------------------------------------------------------------------------
                              |               Robust
                  nonneg_cite | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
------------------------------+----------------------------------------------------------------
                  ingroupBuck |
                 Same Gender  |  -.0113277   .0297944    -0.38   0.704    -.0697237    .0470682
                   Same Race  |   .0468395   .0249575     1.88   0.061    -.0020763    .0957554
                  Same Party  |   .0006022   .0347501     0.02   0.986    -.0675067    .0687111
        Same Gender and Race  |   .0550624   .0263538     2.09   0.037       .00341    .1067148
       Same Gender and Party  |    .011031   .0339897     0.32   0.746    -.0555875    .0776495
         Same Race and Party  |   .0896836   .0253413     3.54   0.000     .0400156    .1393516
Same Race, Gender, and Party  |   .0839075   .0252121     3.33   0.001     .0344927    .1333222
                              |
                sameAgeCohort |   .0211229   .0109015     1.94   0.053    -.0002437    .0424895
                 sameSubIssue |    .438302   .0200117    21.90   0.000     .3990798    .4775243
              id_opposed_prec |  -.0026616   .0106808    -0.25   0.803    -.0235956    .0182724
                 citing_chief |   .0054689   .0523957     0.10   0.917    -.0972249    .1081627
              citing_fresh_au |  -.1106271   .0682192    -1.62   0.105    -.2443343    .0230802
         ln_citing_word_count |   .2071908    .013595    15.24   0.000     .1805452    .2338365
                  citing_year |    -.01272   .0029047    -4.38   0.000    -.0184132   -.0070268
                cited_dissent |   .0514729   .0129349     3.98   0.000      .026121    .0768247
               cited_prec_age |  -.0206473   .0017371   -11.89   0.000     -.024052   -.0172426
          ln_cited_word_count |   .0660885   .0086074     7.68   0.000     .0492182    .0829588
                        _cons |   19.74558    5.82223     3.39   0.001      8.33422    31.15694
-----------------------------------------------------------------------------------------------

. estimates store mod2c

. probit nonneg_cite  c.sameGenderB c.sameRaceB c.sameParty sameAgeCohort  sameSubIssue id_opposed_prec citing_chief citing_fr
> esh_au ln_citing_word_count  citing_year cited_dissent cited_prec_age ln_cited_word_count,  robust cluster(citing_caseID)  

Iteration 0:   log pseudolikelihood = -30781.239  
Iteration 1:   log pseudolikelihood =  -29748.89  
Iteration 2:   log pseudolikelihood = -29643.683  
Iteration 3:   log pseudolikelihood = -29643.141  
Iteration 4:   log pseudolikelihood = -29643.141  

Probit regression                                    Number of obs = 5,215,559
                                                     Wald chi2(13) =    959.49
                                                     Prob > chi2   =    0.0000
Log pseudolikelihood = -29643.141                    Pseudo R2     =    0.0370

                              (Std. err. adjusted for 3,695 clusters in citing_caseID)
--------------------------------------------------------------------------------------
                     |               Robust
         nonneg_cite | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
---------------------+----------------------------------------------------------------
         sameGenderB |  -.0024175   .0139101    -0.17   0.862    -.0296808    .0248457
           sameRaceB |   .0658555   .0179342     3.67   0.000     .0307052    .1010058
           sameParty |   .0350139   .0104085     3.36   0.001     .0146135    .0554142
       sameAgeCohort |   .0212294   .0108589     1.96   0.051    -.0000538    .0425125
        sameSubIssue |   .4382341   .0200144    21.90   0.000     .3990066    .4774615
     id_opposed_prec |  -.0037009   .0106242    -0.35   0.728    -.0245239    .0171221
        citing_chief |   .0079605   .0523778     0.15   0.879    -.0946981    .1106191
     citing_fresh_au |  -.1092044   .0682921    -1.60   0.110    -.2430544    .0246456
ln_citing_word_count |   .2061522   .0135655    15.20   0.000     .1795643    .2327401
         citing_year |  -.0127663   .0029064    -4.39   0.000    -.0184626   -.0070699
       cited_dissent |   .0510733   .0129279     3.95   0.000      .025735    .0764116
      cited_prec_age |   -.020547   .0017346   -11.85   0.000    -.0239467   -.0171473
 ln_cited_word_count |   .0652328   .0086076     7.58   0.000     .0483622    .0821033
               _cons |    19.8378   5.825957     3.41   0.001     8.419133    31.25646
--------------------------------------------------------------------------------------

. estimates store mod3c

. estout mod1c mod2c mod3c, cells("b(star fmt(3)) se(fmt(3) par)")  starlevels(* 0.05)  stats( N aic bic)  

---------------------------------------------------------------------------------------------
                    mod1c                      mod2c                      mod3c              
                        b            se            b            se            b            se
---------------------------------------------------------------------------------------------
nonneg_cite                                                                                  
numSame             0.030*      (0.007)                                                      
sameAgeCoh~t        0.021*      (0.011)        0.021       (0.011)        0.021       (0.011)
sameSubIssue        0.438*      (0.020)        0.438*      (0.020)        0.438*      (0.020)
id_opposed~c       -0.005       (0.011)       -0.003       (0.011)       -0.004       (0.011)
citing_chief        0.006       (0.052)        0.005       (0.052)        0.008       (0.052)
citing_fre~u       -0.109       (0.068)       -0.111       (0.068)       -0.109       (0.068)
ln_citing_~t        0.206*      (0.014)        0.207*      (0.014)        0.206*      (0.014)
citing_year        -0.013*      (0.003)       -0.013*      (0.003)       -0.013*      (0.003)
cited_diss~t        0.051*      (0.013)        0.051*      (0.013)        0.051*      (0.013)
cited_prec~e       -0.021*      (0.002)       -0.021*      (0.002)       -0.021*      (0.002)
ln_cited_w~t        0.065*      (0.009)        0.066*      (0.009)        0.065*      (0.009)
0.ingroupB~k                                   0.000           (.)                           
1.ingroupB~k                                  -0.011       (0.030)                           
2.ingroupB~k                                   0.047       (0.025)                           
3.ingroupB~k                                   0.001       (0.035)                           
4.ingroupB~k                                   0.055*      (0.026)                           
5.ingroupB~k                                   0.011       (0.034)                           
6.ingroupB~k                                   0.090*      (0.025)                           
7.ingroupB~k                                   0.084*      (0.025)                           
sameGenderB                                                              -0.002       (0.014)
sameRaceB                                                                 0.066*      (0.018)
sameParty                                                                 0.035*      (0.010)
_cons              20.028*      (5.839)       19.746*      (5.822)       19.838*      (5.826)
---------------------------------------------------------------------------------------------
N             5215559.000                5215559.000                5215559.000              
aic             59329.144                  59318.851                  59314.281              
bic             59490.750                  59561.260                  59502.821              
---------------------------------------------------------------------------------------------

. 
. ** Table 6: Models with only top half of cosine data
. sum cosine100, d

                      Cosine Similarity
-------------------------------------------------------------
      Percentiles      Smallest
 1%     2.154969       1.285056
 5%     2.806351       1.285402
10%     3.243542       1.287025       Obs           5,215,559
25%      4.12724       1.288353       Sum of wgt.   5,215,559

50%     5.436009                      Mean           6.075696
                        Largest       Std. dev.      3.068448
75%     7.202237       80.13217
90%      9.48215       82.26493       Variance       9.415376
95%     11.37331       82.69995       Skewness       3.150495
99%      16.9542       83.16205       Kurtosis       28.70693

. probit nonneg_cite numSame sameAgeCohort sameSubIssue id_opposed_prec  citing_chief citing_fresh_au ln_citing_word_count  ci
> ting_year cited_dissent cited_prec_age ln_cited_word_count if cosine100 > 5.44,  robust cluster(citing_caseID)

Iteration 0:   log pseudolikelihood = -26626.636  
Iteration 1:   log pseudolikelihood = -25980.475  
Iteration 2:   log pseudolikelihood = -25935.678  
Iteration 3:   log pseudolikelihood = -25935.578  
Iteration 4:   log pseudolikelihood = -25935.578  

Probit regression                                    Number of obs = 2,604,050
                                                     Wald chi2(11) =    654.17
                                                     Prob > chi2   =    0.0000
Log pseudolikelihood = -25935.578                    Pseudo R2     =    0.0260

                              (Std. err. adjusted for 3,693 clusters in citing_caseID)
--------------------------------------------------------------------------------------
                     |               Robust
         nonneg_cite | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
---------------------+----------------------------------------------------------------
             numSame |   .0295311   .0080776     3.66   0.000     .0136993    .0453629
       sameAgeCohort |   .0280306   .0117191     2.39   0.017     .0050615    .0509996
        sameSubIssue |   .3828446   .0220081    17.40   0.000     .3397095    .4259797
     id_opposed_prec |  -.0008948   .0115295    -0.08   0.938    -.0234922    .0217026
        citing_chief |   .0058862   .0619484     0.10   0.924    -.1155305    .1273028
     citing_fresh_au |  -.1114277   .0736733    -1.51   0.130    -.2558248    .0329693
ln_citing_word_count |   .1573824   .0146835    10.72   0.000     .1286033    .1861615
         citing_year |   -.008294   .0031377    -2.64   0.008    -.0144438   -.0021442
       cited_dissent |   .0503257   .0139206     3.62   0.000     .0230419    .0776095
      cited_prec_age |  -.0223825   .0018705   -11.97   0.000    -.0260486   -.0187165
 ln_cited_word_count |   .0540442   .0092382     5.85   0.000     .0359377    .0721507
               _cons |   11.59369   6.293845     1.84   0.065    -.7420239     23.9294
--------------------------------------------------------------------------------------

. estimates store mod1e

. probit nonneg_cite  i.ingroupBuck  sameAgeCohort  sameSubIssue id_opposed_prec citing_chief citing_fresh_au ln_citing_word_c
> ount  citing_year cited_dissent cited_prec_age ln_cited_word_count if cosine100 > 5.44,  robust cluster(citing_caseID)  

Iteration 0:   log pseudolikelihood = -26626.636  
Iteration 1:   log pseudolikelihood = -25970.361  
Iteration 2:   log pseudolikelihood =  -25924.48  
Iteration 3:   log pseudolikelihood = -25924.376  
Iteration 4:   log pseudolikelihood = -25924.376  

Probit regression                                    Number of obs = 2,604,050
                                                     Wald chi2(17) =    666.35
                                                     Prob > chi2   =    0.0000
Log pseudolikelihood = -25924.376                    Pseudo R2     =    0.0264

                                       (Std. err. adjusted for 3,693 clusters in citing_caseID)
-----------------------------------------------------------------------------------------------
                              |               Robust
                  nonneg_cite | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
------------------------------+----------------------------------------------------------------
                  ingroupBuck |
                 Same Gender  |  -.0389853   .0322656    -1.21   0.227    -.1022247    .0242541
                   Same Race  |   .0272808    .026906     1.01   0.311    -.0254539    .0800156
                  Same Party  |  -.0142833   .0378708    -0.38   0.706    -.0885087     .059942
        Same Gender and Race  |   .0410797   .0285808     1.44   0.151    -.0149376    .0970971
       Same Gender and Party  |  -.0115005   .0368885    -0.31   0.755    -.0838007    .0607996
         Same Race and Party  |   .0777348     .02772     2.80   0.005     .0234045     .132065
Same Race, Gender, and Party  |   .0656321   .0273701     2.40   0.016     .0119877    .1192764
                              |
                sameAgeCohort |   .0272931   .0117831     2.32   0.021     .0041987    .0503876
                 sameSubIssue |   .3839845   .0220249    17.43   0.000     .3408165    .4271525
              id_opposed_prec |   .0009699    .011565     0.08   0.933    -.0216971     .023637
                 citing_chief |   .0069347   .0618983     0.11   0.911    -.1143838    .1282532
              citing_fresh_au |  -.1134144   .0737084    -1.54   0.124    -.2578803    .0310514
         ln_citing_word_count |    .158549   .0147075    10.78   0.000     .1297227    .1873752
                  citing_year |  -.0081903   .0031285    -2.62   0.009     -.014322   -.0020585
                cited_dissent |   .0501459   .0139303     3.60   0.000      .022843    .0774488
               cited_prec_age |  -.0224156   .0018744   -11.96   0.000    -.0260893   -.0187419
          ln_cited_word_count |   .0551932   .0092552     5.96   0.000     .0370533    .0733331
                        _cons |   11.38775   6.274632     1.81   0.070    -.9103046     23.6858
-----------------------------------------------------------------------------------------------

. estimates store mod2e

. probit nonneg_cite  c.sameGenderB c.sameRaceB c.sameParty sameAgeCohort  sameSubIssue id_opposed_prec citing_chief citing_fr
> esh_au ln_citing_word_count  citing_year cited_dissent cited_prec_age ln_cited_word_count if cosine100 > 5.44,  robust clust
> er(citing_caseID)  

Iteration 0:   log pseudolikelihood = -26626.636  
Iteration 1:   log pseudolikelihood = -25971.916  
Iteration 2:   log pseudolikelihood = -25926.239  
Iteration 3:   log pseudolikelihood = -25926.135  
Iteration 4:   log pseudolikelihood = -25926.135  

Probit regression                                    Number of obs = 2,604,050
                                                     Wald chi2(13) =    663.86
                                                     Prob > chi2   =    0.0000
Log pseudolikelihood = -25926.135                    Pseudo R2     =    0.0263

                              (Std. err. adjusted for 3,693 clusters in citing_caseID)
--------------------------------------------------------------------------------------
                     |               Robust
         nonneg_cite | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
---------------------+----------------------------------------------------------------
         sameGenderB |  -.0062556   .0149657    -0.42   0.676    -.0355879    .0230766
           sameRaceB |   .0670695   .0194311     3.45   0.001     .0289853    .1051536
           sameParty |   .0355662   .0114463     3.11   0.002     .0131318    .0580006
       sameAgeCohort |   .0278271   .0117192     2.37   0.018     .0048578    .0507964
        sameSubIssue |   .3836898   .0220334    17.41   0.000     .3405052    .4268744
     id_opposed_prec |    .000351   .0115173     0.03   0.976    -.0222226    .0229245
        citing_chief |   .0085174   .0618795     0.14   0.891    -.1127642    .1297991
     citing_fresh_au |  -.1122337   .0737978    -1.52   0.128    -.2568748    .0324074
ln_citing_word_count |   .1576671   .0146809    10.74   0.000     .1288931    .1864412
         citing_year |  -.0081989   .0031292    -2.62   0.009     -.014332   -.0020658
       cited_dissent |   .0496804   .0139217     3.57   0.000     .0223944    .0769664
      cited_prec_age |  -.0223406   .0018728   -11.93   0.000    -.0260113   -.0186699
 ln_cited_word_count |   .0544694   .0092414     5.89   0.000     .0363565    .0725823
               _cons |   11.38715   6.276369     1.81   0.070    -.9143068    23.68861
--------------------------------------------------------------------------------------

. estimates store mod3e

. estout mod1e mod2e mod3e, cells("b(star fmt(3)) se(fmt(3) par)")  starlevels(* 0.05)  stats( N aic bic) label

-----------------------------------------------------------------------------------------------------
                            mod1e                      mod2e                      mod3e              
                                b            se            b            se            b            se
-----------------------------------------------------------------------------------------------------
nonneg_cite                                                                                          
Number of Shared I~s        0.030*      (0.008)                                                      
Same Generation             0.028*      (0.012)        0.027*      (0.012)        0.028*      (0.012)
Same Sub Issue              0.383*      (0.022)        0.384*      (0.022)        0.384*      (0.022)
Ideologically Oppo~t       -0.001       (0.012)        0.001       (0.012)        0.000       (0.012)
Chief Judge                 0.006       (0.062)        0.007       (0.062)        0.009       (0.062)
New Judge                  -0.111       (0.074)       -0.113       (0.074)       -0.112       (0.074)
Logged Word Count,~n        0.157*      (0.015)        0.159*      (0.015)        0.158*      (0.015)
Opinion Year               -0.008*      (0.003)       -0.008*      (0.003)       -0.008*      (0.003)
Precedent Not Unan~s        0.050*      (0.014)        0.050*      (0.014)        0.050*      (0.014)
Age                        -0.022*      (0.002)       -0.022*      (0.002)       -0.022*      (0.002)
Logged Word Count,~t        0.054*      (0.009)        0.055*      (0.009)        0.054*      (0.009)
No Shared Identities                                   0.000           (.)                           
Same Gender                                           -0.039       (0.032)                           
Same Race                                              0.027       (0.027)                           
Same Party                                            -0.014       (0.038)                           
Same Gender and Race                                   0.041       (0.029)                           
Same Gender and Pa~y                                  -0.012       (0.037)                           
Same Race and Party                                    0.078*      (0.028)                           
Same Race, Gender,~y                                   0.066*      (0.027)                           
Same Gender                                                                      -0.006       (0.015)
Same Race                                                                         0.067*      (0.019)
Same Party                                                                        0.036*      (0.011)
_cons                      11.594       (6.294)       11.388       (6.275)       11.387       (6.276)
-----------------------------------------------------------------------------------------------------
N                     2604050.000                2604050.000                2604050.000              
aic                     51895.155                  51884.752                  51880.270              
bic                     52048.426                  52114.658                  52059.086              
-----------------------------------------------------------------------------------------------------

. 
. *** Table 7: Main model with shared professional experiences
. probit nonneg_cite numSame sameAgeCohort cosine100 sameSubIssue id_opposed_prec citingPanAu citing_chief citing_fresh_au ln_
> citing_word_count  citing_year cited_dissent cited_prec_age ln_cited_word_count  bothPros sameLawSchool bothAG bothSG bothTe
> ach,  robust cluster(citing_caseID)

Iteration 0:   log pseudolikelihood = -30781.239  
Iteration 1:   log pseudolikelihood = -29540.954  
Iteration 2:   log pseudolikelihood = -25031.888  
Iteration 3:   log pseudolikelihood = -25013.664  
Iteration 4:   log pseudolikelihood = -25013.616  
Iteration 5:   log pseudolikelihood = -25013.616  

Probit regression                                    Number of obs = 5,215,559
                                                     Wald chi2(18) =   5434.86
                                                     Prob > chi2   =    0.0000
Log pseudolikelihood = -25013.616                    Pseudo R2     =    0.1874

                              (Std. err. adjusted for 3,695 clusters in citing_caseID)
--------------------------------------------------------------------------------------
                     |               Robust
         nonneg_cite | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
---------------------+----------------------------------------------------------------
             numSame |   .0253463   .0081976     3.09   0.002     .0092792    .0414134
       sameAgeCohort |   .0306742   .0119967     2.56   0.011     .0071611    .0541873
           cosine100 |   .0648547   .0009343    69.42   0.000     .0630236    .0666858
        sameSubIssue |   .3528047   .0213272    16.54   0.000     .3110041    .3946053
     id_opposed_prec |  -.0103731   .0117419    -0.88   0.377    -.0333869    .0126406
         citingPanAu |   .4152787    .196091     2.12   0.034     .0309475      .79961
        citing_chief |  -.0556881   .0509137    -1.09   0.274    -.1554771    .0441009
     citing_fresh_au |  -.0907129   .0718583    -1.26   0.207    -.2315527    .0501268
ln_citing_word_count |   .1682181   .0147693    11.39   0.000     .1392708    .1971655
         citing_year |  -.0110271   .0031493    -3.50   0.000    -.0171997   -.0048546
       cited_dissent |   .0240203   .0144966     1.66   0.098    -.0043926    .0524331
      cited_prec_age |  -.0177134   .0018799    -9.42   0.000    -.0213979   -.0140289
 ln_cited_word_count |   .0591554   .0096487     6.13   0.000     .0402443    .0780665
            bothPros |   .0128494   .0245209     0.52   0.600    -.0352107    .0609096
       sameLawSchool |   .0194262   .0357698     0.54   0.587    -.0506813    .0895337
              bothAG |   .0362813   .0324732     1.12   0.264     -.027365    .0999275
              bothSG |   .0968291   .0892591     1.08   0.278    -.0781154    .2717737
           bothTeach |   .0012982   .0235588     0.06   0.956    -.0448762    .0474726
               _cons |   16.22964   6.319273     2.57   0.010     3.844093    28.61519
--------------------------------------------------------------------------------------

. estimates store mod1d

. estout mod1d , cells("b(star fmt(3)) se(fmt(3) par)")  starlevels(* 0.05)  stats( N aic bic)   label

-----------------------------------------------
                            mod1d              
                                b            se
-----------------------------------------------
nonneg_cite                                    
Number of Shared I~s        0.025*      (0.008)
Same Generation             0.031*      (0.012)
Cosine Similarity           0.065*      (0.001)
Same Sub Issue              0.353*      (0.021)
Ideologically Oppo~t       -0.010       (0.012)
Precedent Author o~l        0.415*      (0.196)
Chief Judge                -0.056       (0.051)
New Judge                  -0.091       (0.072)
Logged Word Count,~n        0.168*      (0.015)
Opinion Year               -0.011*      (0.003)
Precedent Not Unan~s        0.024       (0.014)
Age                        -0.018*      (0.002)
Logged Word Count,~t        0.059*      (0.010)
Both Former Prosec~s        0.013       (0.025)
Same Law School             0.019       (0.036)
bothAG                      0.036       (0.032)
bothSG                      0.097       (0.089)
bothTeach                   0.001       (0.024)
_cons                      16.230*      (6.319)
-----------------------------------------------
N                     5215559.000              
aic                     50065.233              
bic                     50321.109              
-----------------------------------------------

. 
. 
. *** Analysis of aggregated impact
. 
. ** Average potential cites per prec: 1,028
. keep cited_caseID

. gen counter = 1

. egen potCitePerPrec = sum(counter), by (cited_caseID)

. duplicates drop

Duplicates in terms of all variables

(5,210,483 observations deleted)

. sum potCitePerPrec,d

                       potCitePerPrec
-------------------------------------------------------------
      Percentiles      Smallest
 1%            6              1
 5%           37              1
10%           80              1       Obs               5,076
25%          250              1       Sum of wgt.       5,076

50%          713                      Mean           1027.494
                        Largest       Std. dev.      945.6499
75%         1562           3601
90%         2604           3601       Variance       894253.8
95%         3059           3605       Skewness       .9749464
99%         3431           3606       Kurtosis       2.906889

. 
. * Average precedent cited 0.82 times if 3 shared identities
. di (1028 * .0008)
.8224

. * Average precedent cited 0.62 times if 0 shared identities
. di (1028 * .0006)
.6168

. * Estimated cites with 3 shared identities for 1000 precedents: 822
. di (1028 * .0008) * 1000
822.4

. * Estimated cites with 0 shared identities for 1000 precedents: 617
. di (1028 * .0006) * 1000
616.8

. ** Difference between 3 and 0 shared identities: 205
. di 822 - 617
205

. 
. 
. log close
      name:  <unnamed>
       log:  /Users/rachaelhinkle/Library/CloudStorage/Dropbox/Citations_Matthews_Hinkle/code/MH_R&P_replication_files/MH_Judi
> cial_Influence.log
  log type:  text
 closed on:   2 Jun 2025, 10:06:42
------------------------------------------------------------------------------------------------------------------------------
